Course - Advanced Computer Intensive Statistical Methods - MA8702
MA8702 - Advanced Computer Intensive Statistical Methods
About
Examination arrangement
Examination arrangement: Oral exam
Grade: Passed / Not Passed
| Evaluation | Weighting | Duration | Grade deviation | Examination aids |
|---|---|---|---|---|
| Oral exam | 100/100 | 45 minutes | E |
Course content
The course will give a theoretical and methodological introduction and discussion of computational intensive statistical methods, but assumes also good computational skills. Topics to be discussed are a selection of the following; theory and methods for Markov chain Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, graphical models, latent Gaussian models and their approximate Bayesian inference. Relative weighting of the various topics will vary according to need.
Learning outcome
1. Knowledge. The course gives a theoretical and methodological introduction and discussion of computational intensive statistical methods, but assumes also good computational skills. Topics to be discussed are a selection of the following; theory and methods for Markov chain Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, graphical models, latent Gaussian models and their approximate Bayesian inference. 2. Skills. The students should be able to use the basic computational intensive techniques in the modern theoretical statistics. In particular, Markov chain, Monte Carlo, Hidden Markov chains, Gaussian Markov random fields, mixtures, non-parametric methods and regression, splines, graphical models, latent Gaussian models and their approximate Bayesian inference. 3. Competence. The students should be able to participate in scientific discussions and conduct researches in statistics on high international level. They should be able to participate in applied projects involving statistical methods and apply their knowledge in problems in theoretical statistics.
Learning methods and activities
This subject is normally taught every second year, next time spring 2024. A condition is that sufficiently many students are registered. Lectures, alternatively guided self-study if there are only few students. The content and form of the obligatory activities will be given at semester start.
Compulsory assignments
- Obligatory activities
Recommended previous knowledge
TMA4315 Generalized Linear Models
Required previous knowledge
TMA4300 Computer Intensive Statistical Methods, TMA4295 Statistical Inference, TMA4267 Linear statistical models, or equivalent. Good understanding and experience with R, or another high-level programming language.
Course materials
Will be announced at the start of the course.
Version: 1
Credits:
7.5 SP
Study level: Doctoral degree level
Term no.: 1
Teaching semester: SPRING 2024
Language of instruction: English
Location: Trondheim
- Statistics
Department with academic responsibility
Department of Mathematical Sciences
Examination
Examination arrangement: Oral exam
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
-
Spring
ORD
Oral exam
100/100
E
Release
2024-05-10Submission
2024-05-10
08:00
08:30 -
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"